To Make a (Metaphysical) World: The "Return to Order" in George Ault's Late Paintings

This thesis examines the paintings of American artist George Ault from the late 1930s until his death in 1948. Questioning earlier appraisals of these images as surrealist, it argues that they are better aligned with the tenets of the Italian metaphysical school and its founding artist, Giorgio de Chirico. Unlike the surrealists, de Chirico espoused a nationalist point of view in his paintings, a tendency that is replicated in Ault's late works. The thesis considers two groups of images: the first is Ault's paintings of the female nude, which repeat the classical allusions found in the paintings of de Chirico. The second is images of Woodstock, New York, in which Ault applies the methodology of the metaphysical school to American subjects, creating nostalgic, imagined views of nineteenth-century rural New York. The conclusion considers how Ault's late paintings complicate scholarly narratives of surrealism's reception in American art before World War II

MODEL-BASED SECURITY ANALYSIS OF FPGA DESIGNS THROUGH REINFORCEMENT LEARNING

Finding potential security weaknesses in any complex IT system is an important and often challenging task best started in the early stages of the development process. We present a method that transforms this task for FPGA designs into a reinforcement learning (RL) problem. This paper introduces a method to generate a Markov Decision Process based RL model from a formal, high-level system description (formulated in the domain-specific language) of the system under review and different, quantified assumptions about the system’s security. Probabilistic transitions and the reward function can be used to model the varying resilience of different elements against attacks and the capabilities of an attacker. This information is then used to determine a plausible data exfiltration strategy. An example with multiple scenarios illustrates the workflow. A discussion of supplementary techniques like hierarchical learning and deep neural networks concludes this paper

Nonparametric tests for detecting breaks in the jump behaviour of a time-continuous process

This paper is concerned with tests for changes in the jump behaviour of a time-continuous process. Based on results on weak convergence of a sequential empirical tail integral process, asymptotics of certain tests statistics for breaks in the jump measure of an Ito semimartingale are constructed. Whenever limiting distributions depend in a complicated way on the unknown jump measure, empirical quantiles are obtained using a multiplier bootstrap scheme. An extensive simulation study shows a good performance of our tests in finite samples.Comment: 29 pages, 4 figure

Das IRB-Modell des Kreditrisikos im Vergleich zum Modell einer logarithmisch normalverteilten Verlustfunktion

In 2004 the Basel Committee published an extensive revision of the capital charges which creates more risk sensitive capital requirements for banks. The New Accord called International Convergence of Capital Measurement and Capital Standard provides in its first pillar for a finer measurement of credit risk. Banks that have received supervisory approval to use the Internal Ratings-Based (IRB) approach may rely on their own internal estimates of risk components in determining the capital requirement for a given exposure. The IRB approach is based on measures of unexpected losses (UL) and expected losses (EL). The risk-weight functions produce capital requirements for the UL portion are based on a onefactor (Merton) model which relies furthermore on the assumption of an infinite fine-grained credit portfolio (also known as Vasicek-Model). As Moody´s stated in 2000: Empirical tests verified the log normal distribution for granular pools. we compared both models in order to benchmark the IRB approach with an existing and in practice already verified model which obviously uses similar assumptions. We, therefore, compute the capital requirement or Credit Value at Risk for given portfolios in both approaches respectively and contrast the results. --Basel II,Expected Loss,Unexpected Loss,Kreditrisikomodell,logarithmische Normalverteiling,Credit Value at Risk

Weak convergence of the empirical truncated distribution function of the Lévy measure of an Itos semimartingale

Given an Ito semimartingale with a time-homogeneous jump part observed at high frequency, we prove weak convergence of a normalized truncated empirical distribution function of the Levy measure to a Gaussian process. In contrast to competing procedures, our estimator works for processes with a non-vanishing diffusion component and under simple assumptions on the jump process

Photoluminescence at room temperature of liquid-phase crystallized silicon on glass

The room temperature photoluminescence (PL) spectrum due band-to-band recombination in an only 8 μm thick liquid-phase crystallized silicon on glass solar cell absorber is measured over 3 orders of magnitude with a thin 400 μm thick optical fiber directly coupled to the spectrometer. High PL signal is achieved by the possibility to capture the PL spectrum very near to the silicon surface. The spectra measured within microcrystals of the absorber present the same features as spectra of crystalline silicon wafers without showing defect luminescence indicating the high electronic material quality of the liquid-phase multi-crystalline layer after hydrogen plasma treatment

Amygdala fMRI Signal as a Predictor of Reaction Time

Reaction times (RTs) are a valuable measure for assessing cognitive processes. However, RTs are susceptible to confounds and therefore variable. Exposure to threat, for example, speeds up or slows down responses. Distinct task types to some extent account for differential effects of threat on RTs. But also do inter-individual differences like trait anxiety. In this functional magnetic resonance imaging (fMRI) study, we investigated whether activation within the amygdala, a brain region closely linked to the processing of threat, may also function as a predictor of RTs, similar to trait anxiety scores. After threat conditioning by means of aversive electric shocks, 45 participants performed a choice RT task during alternating 30 s blocks in the presence of the threat conditioned stimulus [CS+] or of the safe control stimulus [CS-]. Trait anxiety was assessed with the State-Trait Anxiety Inventory and participants were median split into a high- and a low-anxiety subgroup. We tested three hypotheses: (1) RTs will be faster during the exposure to threat compared to the safe condition in individuals with high trait anxiety. (2) The amygdala fMRI signal will be higher in the threat condition compared to the safe condition. (3) Amygdala fMRI signal prior to a RT trial will be correlated with the corresponding RT. We found that, the high-anxious subgroup showed faster responses in the threat condition compared to the safe condition, while the low-anxious subgroup showed no significant difference in RTs in the threat condition compared to the safe condition. Though the fMRI analysis did not reveal an effect of condition on amygdala activity, we found a trial-by-trial correlation between blood-oxygen-level-dependent signal within the right amygdala prior to the CRT task and the subsequent RT. Taken together, the results of this study showed that exposure to threat modulates task performance. This modulation is influenced by personality trait. Additionally and most importantly, activation in the amygdala predicts behavior in a simple task that is performed during the exposure to threat. This finding is in line with “attentional capture by threat”—a model that includes the amygdala as a key brain region for the process that causes the response slowing

Model-driven Security Engineering for FPGAs

Tato práce obsahuje analýzu a adaptaci vhodných metod zabezpečení, pocházejících ze softwarové domény, do světa FPGA. Metoda formalizace bezpečnostní výzvy FPGA je prezentována jazykem FPGASECML, specifickým pro danou doménu, vhodným pro modelování hrozeb zaměřených na systém a pro formální definování bezpečnostní politiky. Vytvoření vhodných obranných mechanismů vyžaduje inteligenci o agentech ohrožení, zejména o jejich motivaci a schopnostech. Konstrukce založené na FPGA jsou, stejně jako jakýkoli jiný IT systém, vystaveny různým agentům hrozeb po celou dobu jejich životnosti, což naléhavě vyžaduje potřebu vhodné a přizpůsobitelné bezpečnostní strategie. Systematická analýza návrhu založená na konceptu STRIDE poskytuje cenné informace o hrozbách a požadovaných mechanismech protiopatření. Minimalizace povrchu útoku je jedním z nezbytných kroků k vytvoření odolného designu. Konvenční paradigmata řízení přístupu mohou modelovat pravidla řízení přístupu v návrzích FPGA. Výběr vhodného závisí na složitosti a bezpečnostních požadavcích návrhu. Formální popis architektury FPGA a bezpečnostní politiky podporuje přesnou definici aktiv a jejich možných, povolených a zakázaných interakcí. Odstraňuje nejednoznačnost z modelu hrozby a zároveň poskytuje plán implementace. Kontrola modelu může být použita k ověření, zda a do jaké míry, je návrh v souladu s uvedenou bezpečnostní politikou. Přenesení architektury do vhodného modelu a bezpečnostní politiky do ověřitelných logických vlastností může být, jak je uvedeno v této práci, automatizované, zjednodušující proces a zmírňující jeden zdroj chyb. Posílení učení může identifikovat potenciální slabiny a kroky, které může útočník podniknout, aby je využil. Některé metody zde uvedené mohou být použitelné také v jiných doménách.ObhájenoThe thesis provides an analysis and adaptation of appropriate security methods from the software domain into the FPGA world and combines them with formal verification methods and machine learning techniques. The deployment of appropriate defense mechanisms requires intelligence about the threat agents, especially their motivation and capabilities. FPGA based designs are, like any other IT system, exposed to different threat agents throughout the systems lifetime, urging the need for a suitable and adaptable security strategy. The systematic analysis of the design, based on the STRIDE concept, provides valuable insight into the threats and the mandated counter mechanisms. Minimizing the attack surface is one essential step to create a resilient design. Conventional access control paradigms can model access control rules in FPGA designs and thereby restrict the exposure of sensitive elements to untrustworthy ones. A method to formalize the FPGA security challenge is presented. FPGASECML is a domain-specific language, suitable for dataflow-centric threat modeling as well as the formal definition of an enforceable security policy. The formal description of the FPGA architecture and the security policy promotes a precise definition of the assets and their possible, allowed, and prohibited interactions. Formalization removes ambiguity from the threat model while providing a blueprint for the implementation. Model transformations allow the application of dedicated and proven tools to answer specific questions while minimizing the workload for the user. Model-checking can be applied to verify if, and to a certain degree when, a design complies with the stated security policy. Transferring the architecture into a suitable model and the security policy into verifiable logic properties can be, as demonstrated in the thesis, automated, simplifying the process and mitigating one source of error. Reinforcement learning, a machine learning method, can identify potential weaknesses and the steps an attacker may take to exploit them. The approach presented uses a Markov Decision Process in combination with a Qlearning algorithm